Face Recognition Systems: A Survey

Kortli, Yassin; Jridi, Maher; Falou, Ayman Al; Atri, Mohamed

doi:10.3390/s20020342

Cited by 419 publications

(186 citation statements)

References 118 publications

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“…The final stage is face recognition where the extracted features in the previous stage are compared with the stored faces in the database as shown in Figure 13. There are two main applications of face recognition, identification, and verification [43,46]. By using this technology, any person who may be infected with COVID-19 could be tracked.…”

Section: Automated Face Recognition (Afr) Technologymentioning

confidence: 99%

Recently employed engineering techniques to reduce the spread of COVID-19 (corona virus disease 2019): a review study

Saman

Eid

2021

IJEECS

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<span>The main challenges of today’s global health care system are to reach to strong healthcare system, to provide effective methods to eliminate the increase in the number of dead and infected with virus of COVID-19. Therefore, during the last few months, the great importance and efficacy of a variety of engineering techniques that have greatly contributed in curbing the spread of the COVID-19, and evenly help to eliminate it according to recent scientific studies was highly prominent. Among these promising technologies in this field we mention, but not limited to, the use of ultraviolet (UV) rays to disinfection of air and surfaces. In addition, thermal imaging technology, which was employed using infrared radiation for monitoring people in crowded areas and human groups to determine who have abnormal temperatures, so that all preventive measures are taken. Robots have also been used and harnessed to perform many tasks that limit the spread of the virus and maintain the integrity of the human element. Last but not least, facial recognition techniques have also been used to limit the spread of this pandemic. Ultraviolet radiation is one of physical therapy modalities that can be used to increase the efficiency of human immune system to fight the virus. In conclusion UV radiation, infrared thermal imaging, robotics, AFR technologies are now widely used to reduce the spread of this virus and manage the outbreak.</span>

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Section: Automated Face Recognition (Afr) Technologymentioning

confidence: 99%

Recently employed engineering techniques to reduce the spread of COVID-19 (corona virus disease 2019): a review study

Saman

Eid

2021

IJEECS

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“…Facial Recognition is one of the many biometry techniques currently available. Despite its relatively higher chance of false alarm compared to systems such as iris recognition or fingerprint reading, facial recognition has grown largely on interest over the last few years for presenting a non-invasive method of identity verification [32].…”

Section: Facial Recognitionmentioning

confidence: 99%

“…The basis to implement Facial Recognition methods are face detection techniques. Currently, the three most popular methods for face detection algorithms are [32]- [34]:…”

Section: Facial Recognitionmentioning

confidence: 99%

LoRa System for Monitoring and Facial Recognition

Medeiros

Carvalho

Lopes

2021

JCIS

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With the increasing tendency of incorporating technology into environments and activities in everyday life, new methods are being proposed to better integrate devices and mankind. Networks designed for monitoring areas through video and image systems are being implemented in several applications. Recent studies have shown interest from both the academy and industry to integrate wireless surveillance networks with lowcost and long range transmission technologies, such as LoRa (Long Range). With the intention to explore this topic, this work presents the development of a system prototype for intelligent monitoring as a basis to future implementation of low-cost LoRabased Wireless Image Sensor Networks (WISN). The developed system consists of a processing unit, a data routing interface and multiple sensor nodes. Its operation is autonomous and initiated through the detection of human presence, by the sensor node, capturing an image of the individuals present at each detection. The data is transmitted through LoRa devices to the central hub, where python-based methods of facial detection and recognition are employed. Several tests were performed to both adjust the system and to verify its efficiency. The results achieved indicate the viability of the proposed low-cost LoRa-based WISN.

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“…Several methods and algorithms have been suggested in the face recognition literature. They can be subdivided into four fundamental approaches depending on the method used for feature extraction and classification: holistic, local, hybrid, and deep learning approaches [ 11 ]. The deep learning class [ 12 ], which applies consecutive layers of information processing arranged hierarchically for representation, learning, and classification, has dramatically increased state-of-the-art performance, especially with unconstrained large-scale databases, and encouraged real-world applications [ 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

Multi-Block Color-Binarized Statistical Images for Single-Sample Face Recognition

Adjabi

Ouahabi

Benzaoui

et al. 2021

Sensors

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Single-Sample Face Recognition (SSFR) is a computer vision challenge. In this scenario, there is only one example from each individual on which to train the system, making it difficult to identify persons in unconstrained environments, mainly when dealing with changes in facial expression, posture, lighting, and occlusion. This paper discusses the relevance of an original method for SSFR, called Multi-Block Color-Binarized Statistical Image Features (MB-C-BSIF), which exploits several kinds of features, namely, local, regional, global, and textured-color characteristics. First, the MB-C-BSIF method decomposes a facial image into three channels (e.g., red, green, and blue), then it divides each channel into equal non-overlapping blocks to select the local facial characteristics that are consequently employed in the classification phase. Finally, the identity is determined by calculating the similarities among the characteristic vectors adopting a distance measurement of the K-nearest neighbors (K-NN) classifier. Extensive experiments on several subsets of the unconstrained Alex and Robert (AR) and Labeled Faces in the Wild (LFW) databases show that the MB-C-BSIF achieves superior and competitive results in unconstrained situations when compared to current state-of-the-art methods, especially when dealing with changes in facial expression, lighting, and occlusion. The average classification accuracies are 96.17% and 99% for the AR database with two specific protocols (i.e., Protocols I and II, respectively), and 38.01% for the challenging LFW database. These performances are clearly superior to those obtained by state-of-the-art methods. Furthermore, the proposed method uses algorithms based only on simple and elementary image processing operations that do not imply higher computational costs as in holistic, sparse or deep learning methods, making it ideal for real-time identification.

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Face Recognition Systems: A Survey

Cited by 419 publications

References 118 publications

Recently employed engineering techniques to reduce the spread of COVID-19 (corona virus disease 2019): a review study

Recently employed engineering techniques to reduce the spread of COVID-19 (corona virus disease 2019): a review study

LoRa System for Monitoring and Facial Recognition

Multi-Block Color-Binarized Statistical Images for Single-Sample Face Recognition

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